






import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				int  ):
	snake_case__					: List[Any]  =    torch.exp(snake_case_  )
	snake_case__					: Optional[int]  =    torch.sum(snake_case_	,    dim=1  )  # sum of exp(x_i)
	snake_case__					: List[str]  =    torch.sum(x * exp_x	,    dim=1  )  # sum of x_i * exp(x_i)
	return torch.log(snake_case_  ) - B / A



class 				SCREAMING_SNAKE_CASE__		(							nn.Module		):





					"""simple docstring"""






					def __init__(   self	:    Union[str, Any]				,						__A	:    Any			):
						super().__init__()
						snake_case__					: int  =    config.output_attentions
						snake_case__					: Any  =    config.output_hidden_states
						snake_case__					: Tuple  =    nn.ModuleList([BertLayer(snake_case__			) for _ in range(config.num_hidden_layers			)]			)
						snake_case__					: Dict  =    nn.ModuleList([BertHighway(snake_case__			) for _ in range(config.num_hidden_layers			)]			)

						snake_case__					: Dict  =    [-1 for _ in range(config.num_hidden_layers			)]



					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    str			):
						if (type(snake_case__			) is float) or (type(snake_case__			) is int):
							for i in range(len(self.early_exit_entropy			)			):
								snake_case__					: List[str]  =    x
						else:
							snake_case__					: int  =    x



					def 			_lowercase	(   self	:    Tuple				,						__A	:    Optional[int]			):
						snake_case__					: List[str]  =    pooler.state_dict()
						for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]			)






					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    Union[str, Any]				,						__A	:    int=None				,						__A	:    Optional[int]=None				,						__A	:    List[str]=None				,						__A	:    Dict=None				,						):
						snake_case__					: int  =    ()
						snake_case__					: int  =    ()
						snake_case__					: Optional[Any]  =    ()
						for i, layer_module in enumerate(self.layer			):
							if self.output_hidden_states:
								snake_case__					: Optional[Any]  =    all_hidden_states + (hidden_states,)

							snake_case__					: Dict  =    layer_module(
							    snake_case__				,						snake_case__				,						head_mask[i]				,						snake_case__				,						snake_case__			)
							snake_case__					: Optional[Any]  =    layer_outputs[0]

							if self.output_attentions:
								snake_case__					: List[str]  =    all_attentions + (layer_outputs[1],)

							snake_case__					: Optional[Any]  =    (hidden_states,)
							if self.output_hidden_states:
								snake_case__					: Optional[Any]  =    current_outputs + (all_hidden_states,)
							if self.output_attentions:
								snake_case__					: Tuple  =    current_outputs + (all_attentions,)

							snake_case__					: List[Any]  =    self.highway[i](snake_case__			)
							# logits, pooled_output

							if not self.training:
								snake_case__					: Any  =    highway_exit[0]
								snake_case__					: List[Any]  =    entropy(snake_case__			)
								snake_case__					: Optional[int]  =    highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								snake_case__					: Union[str, Any]  =    all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
									snake_case__					: Optional[Any]  =    (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
									raise HighwayException(snake_case__				,						i + 1			)
							else:
								snake_case__					: Union[str, Any]  =    all_highway_exits + (highway_exit,)

        # Add last layer
						if self.output_hidden_states:
							snake_case__					: Tuple  =    all_hidden_states + (hidden_states,)

						snake_case__					: Any  =    (hidden_states,)
						if self.output_hidden_states:
							snake_case__					: Union[str, Any]  =    outputs + (all_hidden_states,)
						if self.output_attentions:
							snake_case__					: Union[str, Any]  =    outputs + (all_attentions,)

						snake_case__					: str  =    outputs + (all_highway_exits,)
						return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits



@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "					,  SCREAMING_SNAKE_CASE_					,  )
class 				SCREAMING_SNAKE_CASE__		(							SCREAMING_SNAKE_CASE_		):





					"""simple docstring"""






					def __init__(   self	:    Dict				,						__A	:    List[Any]			):
						super().__init__(snake_case__			)
						snake_case__					: int  =    config

						snake_case__					: Union[str, Any]  =    BertEmbeddings(snake_case__			)
						snake_case__					: List[Any]  =    DeeBertEncoder(snake_case__			)
						snake_case__					: List[Any]  =    BertPooler(snake_case__			)

						self.init_weights()



					def 			_lowercase	(   self	:    Tuple			):
						self.encoder.init_highway_pooler(self.pooler			)



					def 			_lowercase	(   self	:    int			):
						return self.embeddings.word_embeddings



					def 			_lowercase	(   self	:    Optional[int]				,						__A	:    List[Any]			):
						snake_case__					: Union[str, Any]  =    value



					def 			_lowercase	(   self	:    List[Any]				,						__A	:    int			):
						for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(snake_case__			)






					@add_start_docstrings_to_model_forward(snake_case__			)
					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    Dict=None				,						__A	:    Any=None				,						__A	:    Dict=None				,						__A	:    Any=None				,						__A	:    List[str]=None				,						__A	:    Union[str, Any]=None				,						__A	:    str=None				,						__A	:    List[Any]=None				,						):
						if input_ids is not None and inputs_embeds is not None:
							raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"			)
						elif input_ids is not None:
							snake_case__					: Optional[Any]  =    input_ids.size()
						elif inputs_embeds is not None:
							snake_case__					: Union[str, Any]  =    inputs_embeds.size()[:-1]
						else:
							raise ValueError("You have to specify either input_ids or inputs_embeds"			)

						snake_case__					: Dict  =    input_ids.device if input_ids is not None else inputs_embeds.device

						if attention_mask is None:
							snake_case__					: Any  =    torch.ones(snake_case__				,						device=snake_case__			)
						if encoder_attention_mask is None:
							snake_case__					: Optional[Any]  =    torch.ones(snake_case__				,						device=snake_case__			)
						if token_type_ids is None:
							snake_case__					: List[str]  =    torch.zeros(snake_case__				,						dtype=torch.long				,						device=snake_case__			)

						# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
						# ourselves in which case we just need to make it broadcastable to all heads.
						snake_case__					: int  =    self.get_extended_attention_mask(snake_case__				,						snake_case__				,						snake_case__			)

						# If a 2D ou 3D attention mask is provided for the cross-attention
						# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
						if encoder_attention_mask.dim() == 3:
							snake_case__					: List[Any]  =    encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.dim() == 2:
							snake_case__					: int  =    encoder_attention_mask[:, None, None, :]

						snake_case__					: Optional[Any]  =    encoder_extended_attention_mask.to(
						    dtype=next(self.parameters()			).dtype			)  # fp16 compatibility
						snake_case__					: Optional[Any]  =    (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

						# Prepare head mask if needed
						# 1.0 in head_mask indicate we keep the head
						# attention_probs has shape bsz x n_heads x N x N
						# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
						# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
						snake_case__					: Dict  =    self.get_head_mask(snake_case__				,						self.config.num_hidden_layers			)

						snake_case__					: Optional[Any]  =    self.embeddings(
						    input_ids=snake_case__				,						position_ids=snake_case__				,						token_type_ids=snake_case__				,						inputs_embeds=snake_case__			)
						snake_case__					: Optional[int]  =    self.encoder(
						    snake_case__				,						attention_mask=snake_case__				,						head_mask=snake_case__				,						encoder_hidden_states=snake_case__				,						encoder_attention_mask=snake_case__				,						)
						snake_case__					: Optional[Any]  =    encoder_outputs[0]
						snake_case__					: Union[str, Any]  =    self.pooler(snake_case__			)

						snake_case__					: Any  =    (
						    sequence_output,
						    pooled_output,
						) + encoder_outputs[
						    1:
						]  # add hidden_states and attentions if they are here
						return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits



class 				SCREAMING_SNAKE_CASE__		(							SCREAMING_SNAKE_CASE_		):





					"""simple docstring"""






					def __init__(   self	:    Optional[int]				,						__A	:    Dict				,						__A	:    Any			):
						snake_case__					: List[str]  =    message
						snake_case__					: Tuple  =    exit_layer  # start from 1!



class 				SCREAMING_SNAKE_CASE__		(							nn.Module		):





					"""simple docstring"""






					def __init__(   self	:    Union[str, Any]				,						__A	:    Optional[Any]			):
						super().__init__()
						snake_case__					: int  =    BertPooler(snake_case__			)
						snake_case__					: List[Any]  =    nn.Dropout(config.hidden_dropout_prob			)
						snake_case__					: Union[str, Any]  =    nn.Linear(config.hidden_size				,						config.num_labels			)






					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    Dict			):
						# Pooler
						snake_case__					: int  =    encoder_outputs[0]
						snake_case__					: List[str]  =    self.pooler(snake_case__			)
						# "return" pooler_output

						# BertModel
						snake_case__					: Optional[int]  =    (pooler_input, pooler_output) + encoder_outputs[1:]
						# "return" bmodel_output

						# Dropout and classification
						snake_case__					: Dict  =    bmodel_output[1]

						snake_case__					: List[Any]  =    self.dropout(snake_case__			)
						snake_case__					: List[Any]  =    self.classifier(snake_case__			)

						return logits, pooled_output



@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "					,  SCREAMING_SNAKE_CASE_					,  )
class 				SCREAMING_SNAKE_CASE__		(							SCREAMING_SNAKE_CASE_		):





					"""simple docstring"""






					def __init__(   self	:    Optional[int]				,						__A	:    Dict			):
						super().__init__(snake_case__			)
						snake_case__					: Any  =    config.num_labels
						snake_case__					: Tuple  =    config.num_hidden_layers

						snake_case__					: Optional[Any]  =    DeeBertModel(snake_case__			)
						snake_case__					: Tuple  =    nn.Dropout(config.hidden_dropout_prob			)
						snake_case__					: Any  =    nn.Linear(config.hidden_size				,						self.config.num_labels			)

						self.init_weights()






					@add_start_docstrings_to_model_forward(snake_case__			)
					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    Union[str, Any]=None				,						__A	:    Optional[int]=None				,						__A	:    str=None				,						__A	:    Dict=None				,						__A	:    str=None				,						__A	:    Union[str, Any]=None				,						__A	:    int=None				,						__A	:    Union[str, Any]=-1				,						__A	:    Any=False				,						):
						snake_case__					: int  =    self.num_layers
						try:
							snake_case__					: Dict  =    self.bert(
							    snake_case__				,						attention_mask=snake_case__				,						token_type_ids=snake_case__				,						position_ids=snake_case__				,						head_mask=snake_case__				,						inputs_embeds=snake_case__				,						)
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							snake_case__					: Optional[int]  =    outputs[1]

							snake_case__					: Any  =    self.dropout(snake_case__			)
							snake_case__					: Any  =    self.classifier(snake_case__			)
							snake_case__					: Optional[int]  =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
							snake_case__					: Tuple  =    e.message
							snake_case__					: Optional[Any]  =    e.exit_layer
							snake_case__					: Any  =    outputs[0]

						if not self.training:
							snake_case__					: Tuple  =    entropy(snake_case__			)
							snake_case__					: Optional[int]  =    []
							snake_case__					: Tuple  =    []
						if labels is not None:
							if self.num_labels == 1:
								#  We are doing regression
								snake_case__					: Any  =    MSELoss()
								snake_case__					: Optional[int]  =    loss_fct(logits.view(-1			)				,						labels.view(-1			)			)
							else:
								snake_case__					: List[str]  =    CrossEntropyLoss()
								snake_case__					: Optional[Any]  =    loss_fct(logits.view(-1				,						self.num_labels			)				,						labels.view(-1			)			)

							# work with highway exits
							snake_case__					: Any  =    []
							for highway_exit in outputs[-1]:
								snake_case__					: Optional[Any]  =    highway_exit[0]
								if not self.training:
									highway_logits_all.append(snake_case__			)
									highway_entropy.append(highway_exit[2]			)
								if self.num_labels == 1:
									#  We are doing regression
									snake_case__					: Optional[Any]  =    MSELoss()
									snake_case__					: Union[str, Any]  =    loss_fct(highway_logits.view(-1			)				,						labels.view(-1			)			)
								else:
									snake_case__					: Optional[int]  =    CrossEntropyLoss()
									snake_case__					: List[str]  =    loss_fct(highway_logits.view(-1				,						self.num_labels			)				,						labels.view(-1			)			)
								highway_losses.append(snake_case__			)

							if train_highway:
								snake_case__					: List[Any]  =    (sum(highway_losses[:-1]			),) + outputs
								# exclude the final highway, of course
							else:
								snake_case__					: Tuple  =    (loss,) + outputs
						if not self.training:
							snake_case__					: Any  =    outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
								snake_case__					: int  =    (
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]							)   ->    Optional[Any]:
		_lowerCamelCase				   =  3_84
		_lowerCamelCase				   =  7
		if "tiny" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 6, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "small" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "base" in model_name:
				_lowerCamelCase				   =  1_28
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (4, 8, 16, 32)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  5_12
		elif "large" in model_name:
				_lowerCamelCase				   =  1_92
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (6, 12, 24, 48)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  7_68

		# set label information
		_lowerCamelCase				   =  1_50
		_lowerCamelCase				   =  'huggingface/label-files'
		_lowerCamelCase				   =  'ade20k-id2label.json'
		_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
		_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
		_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		_lowerCamelCase				   =  SwinConfig(
		    embed_dim=UpperCamelCase			,       depths=UpperCamelCase			,       num_heads=UpperCamelCase			,       window_size=UpperCamelCase			,       out_features=['stage1', 'stage2', 'stage3', 'stage4']			,       )
		_lowerCamelCase				   =  UperNetConfig(
		    backbone_config=UpperCamelCase			,       auxiliary_in_channels=UpperCamelCase			,       num_labels=UpperCamelCase			,       idalabel=UpperCamelCase			,       labelaid=UpperCamelCase			,       )

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  []

		# fmt: off
		# stem
		rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')							)
		rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')							)
		rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')							)
		rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')							)
		# stages
		for i in range(len(config.backbone_config.depths							)							):
				for j in range(config.backbone_config.depths[i]							):
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")							)

				if i < 3:
						rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")							)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")							)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")							)

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
		# fmt: on

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Any							)   ->    str:
		_lowerCamelCase				   =  dct.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Tuple							)   ->    str:
		_lowerCamelCase				   =  [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
		for i in range(len(backbone_config.depths							)							):
				_lowerCamelCase				   =  num_features[i]
				for j in range(backbone_config.depths[i]							):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""							)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""							)
						# next, add query, keys and values (in that order) to the state dict
						_lowerCamelCase				   =  in_proj_weight[:dim, :]
						_lowerCamelCase				   =  in_proj_bias[: dim]
						_lowerCamelCase				   =  in_proj_weight[
						    dim : dim * 2, :
						]
						_lowerCamelCase				   =  in_proj_bias[
						    dim : dim * 2
						]
						_lowerCamelCase				   =  in_proj_weight[
						    -dim :, :
						]
						_lowerCamelCase				   =  in_proj_bias[-dim :]
						# fmt: on


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[int]:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3], :].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    int:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, :, [0, 2, 1, 3]].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)

		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    List[str]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[[0, 2, 1, 3], :].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3]].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Dict							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  {
		    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
		    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
		    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
		    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
		}
		_lowerCamelCase				   =  model_name_to_url[model_name]
		_lowerCamelCase				   =  torch.hub.load_state_dict_from_url(UpperCamelCase			,       map_location='cpu'			,       file_name=UpperCamelCase							)[
		    'state_dict'
		]

		for name, param in state_dict.items():
				print(UpperCamelCase			,       param.shape							)

		_lowerCamelCase				   =  get_upernet_config(UpperCamelCase							)
		_lowerCamelCase				   =  UperNetForSemanticSegmentation(UpperCamelCase							)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
				if "bn" in key:
						_lowerCamelCase				   =  key.replace('bn'			,       'batch_norm'							)
				_lowerCamelCase				   =  val

		# rename keys
		_lowerCamelCase				   =  create_rename_keys(UpperCamelCase							)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		read_in_q_k_v(UpperCamelCase			,       config.backbone_config							)

		# fix downsample parameters
		for key, value in state_dict.items():
				if "downsample" in key:
						if "reduction" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_reduction_order(UpperCamelCase							)
						if "norm" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_norm_order(UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)

		# verify on image
		_lowerCamelCase				   =  'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							).convert('RGB'							)

		_lowerCamelCase				   =  SegformerImageProcessor()
		_lowerCamelCase				   =  processor(UpperCamelCase			,       return_tensors='pt'							).pixel_values

		with torch.no_grad():
				_lowerCamelCase				   =  model(UpperCamelCase							)
				_lowerCamelCase				   =  outputs.logits

		print(logits.shape							)
		print('First values of logits:'			,       logits[0, 0, :3, :3]							)
		# assert values
		if model_name == "upernet-swin-tiny":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]							)
		elif model_name == "upernet-swin-small":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]							)
		elif model_name == "upernet-swin-base":
				_lowerCamelCase				   =  torch.tensor(
				    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]							)
		elif model_name == "upernet-swin-large":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]							)
		print('Logits:'			,       outputs.logits[0, 0, :3, :3]							)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]			,       UpperCamelCase			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(UpperCamelCase							)
				print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				print(F"""Pushing model and processor for {model_name} to hub"""							)
				model.push_to_hub(F"""openmmlab/{model_name}"""							)
				processor.push_to_hub(F"""openmmlab/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-swin-tiny',
				    type=str,
				    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
				    help='Name of the Swin + UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)